Köse, BayramAygün, HilmiPak, Semih2023-03-222023-03-22202313001884https://doi.org/10.17341/gazimmfd.935689https://hdl.handle.net/20.500.14034/887In order to meet the increasing energy demand and to solve environmental problems, the interest in renewable energy sources continues with technology development studies and economic investments. Various methods are used to determine and estimate sustainable and renewable energy sources. Probability distribution functions are used in wind characterization and potential calculation of wind energy. In this study, the Dragonfly Algorithm (DA) is proposed to estimate the Weibull probability distribution function (Wpdf) parameters used in wind speed modeling and the two-component mixed Weibull distribution parameters used in modeling non-single peak wind speed data. The performance of the proposed method has been evaluated by comparing not only the classical methods which are the moment method (MM) and the least squares method (LSM) but also metaheuristic optimization algorithms which are Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Determination coefficient (R2) and root mean square error (RMSE) were used to evaluate the performance of these parameter estimation methods. Data obtained from 6 measurement stations were used in the study. According to the performance criteria, the two-component Weibull distribution was found to be more effective at all stations compared to the Weibull distribution model. It has been concluded that the proposed DA algorithm can be used effectively for parameter estimation in wind speed modeling. © 2023 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved.trinfo:eu-repo/semantics/openAccessdragonfly algorithmestimationWeibull distribution parameterswind energy potentialDistribution functionsGenetic algorithmsInvestmentsLeast squares approximationsMean square errorMethod of momentsParameter estimationParticle swarm optimization (PSO)Wind powerWind speedDistribution modelsDistribution parametersDragonfly algorithmParameters estimationProbability distribution functionsRenewable energy sourceTwo-componentWeibull distribution parameterWind energy potentialWind speed modelsWeibull distributionArticle10.17341/gazimmfd.935689383174717562-s2.0-85146833057Q2